from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

Daal4py_kmeans_short: 0h 0m 1s

Daal4py_ridge: 0h 0m 1s

Kmeans_short: 0h 0m 2s

Daal4py_logisticregression: 0h 0m 3s

Daal4py_kmeans_tall: 0h 0m 7s

Ridge: 0h 0m 10s

Logisticregression: 0h 0m 18s

Kmeans_tall: 0h 0m 20s

Daal4py_kneighborsclassifier_kd_tree: 0h 0m 24s

Daal4py_kneighborsclassifier: 0h 2m 20s

Kneighborsclassifier_kd_tree: 0h 2m 20s

Catboost: 0h 5m 1s

Xgboost: 0h 5m 2s

Lightgbm: 0h 5m 5s

Histgradientboostingclassifier: 0h 5m 15s

Catboost_symmetric: 0h 5m 22s

Kneighborsclassifier: 0h 40m 14s

Total: 1h 12m 13s

print_env_info()

Benchmark environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config_file_path="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.134	0.000	5.953	0.000	1	1	NaN	NaN	0.463	0.000	0.290	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	11.880	0.083	0.000	0.012	1	1	0.687	0.831	1.656	0.008	7.173	0.060	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.174	0.000	0.000	0.174	1	1	1.000	1.000	0.077	0.000	2.263	0.012	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.126	0.000	6.371	0.000	-1	100	NaN	NaN	0.449	0.000	0.280	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.708	0.000	0.000	0.033	-1	100	0.950	0.936	1.715	0.005	19.077	0.056	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.163	0.012	0.000	0.163	-1	100	1.000	1.000	0.077	0.000	2.124	0.151	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.123	0.000	6.480	0.000	-1	5	NaN	NaN	0.448	0.000	0.275	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.488	0.000	0.000	0.032	-1	5	0.822	0.936	1.716	0.006	18.932	0.063	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.162	0.015	0.000	0.162	-1	5	1.000	1.000	0.077	0.000	2.096	0.189	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.593	0.000	-1	1	NaN	NaN	0.450	0.000	0.270	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.241	0.104	0.000	0.022	-1	1	0.687	0.831	1.655	0.005	13.442	0.075	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.155	0.017	0.000	0.155	-1	1	1.000	1.000	0.076	0.000	2.026	0.223	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.119	0.000	6.740	0.000	1	100	NaN	NaN	0.449	0.000	0.265	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.094	0.020	0.000	0.022	1	100	0.950	0.713	1.654	0.007	13.355	0.061	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.185	0.001	0.000	0.185	1	100	1.000	0.000	0.077	0.001	2.392	0.034	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.119	0.000	6.746	0.000	1	5	NaN	NaN	0.449	0.000	0.264	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.136	0.031	0.000	0.022	1	5	0.822	0.713	1.658	0.014	13.353	0.116	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.001	0.000	0.183	1	5	1.000	0.000	0.079	0.008	2.315	0.226	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.304	0.000	1	1	NaN	NaN	0.095	0.000	0.554	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.395	0.021	0.000	0.009	1	1	0.988	0.986	0.254	0.000	36.922	0.097	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.013	0.000	0.000	0.013	1	1	1.000	1.000	0.005	0.000	2.611	0.154	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.309	0.000	-1	100	NaN	NaN	0.095	0.000	0.547	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	29.418	0.095	0.000	0.029	-1	100	0.989	0.986	0.302	0.002	97.425	0.727	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.002	0.000	0.024	-1	100	1.000	1.000	0.005	0.000	4.656	0.490	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.310	0.000	-1	5	NaN	NaN	0.095	0.000	0.545	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	29.550	0.119	0.000	0.030	-1	5	0.991	0.986	0.302	0.002	97.876	0.785	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.001	0.000	0.023	-1	5	1.000	1.000	0.005	0.000	4.562	0.354	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.306	0.000	-1	1	NaN	NaN	0.095	0.000	0.550	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.764	0.072	0.000	0.020	-1	1	0.988	0.986	0.255	0.001	77.452	0.326	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.017	0.002	0.000	0.017	-1	1	1.000	1.000	0.005	0.000	3.516	0.533	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.308	0.000	1	100	NaN	NaN	0.095	0.000	0.546	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.040	0.008	0.000	0.019	1	100	0.989	0.980	0.254	0.002	74.986	0.563	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.000	0.000	0.020	1	100	1.000	1.000	0.005	0.000	3.859	0.230	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.303	0.000	1	5	NaN	NaN	0.095	0.000	0.559	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.012	0.013	0.000	0.019	1	5	0.991	0.980	0.253	0.000	75.088	0.122	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.019	0.001	0.000	0.019	1	5	1.000	1.000	0.005	0.000	3.703	0.297	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.640	0.000	0.030	0.000	-1	5	NaN	NaN	0.657	0.000	4.016	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.780	0.003	0.000	0.001	-1	5	0.975	0.978	0.163	0.002	4.794	0.073	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	10.100	4.876	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.665	0.000	0.030	0.000	1	1	NaN	NaN	0.634	0.000	4.207	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.674	0.004	0.000	0.001	1	1	0.948	0.978	0.162	0.001	4.163	0.035	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	3.320	1.637	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.636	0.000	0.030	0.000	-1	1	NaN	NaN	0.626	0.000	4.212	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.417	0.004	0.000	0.000	-1	1	0.948	0.959	0.090	0.001	4.658	0.059	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	11.737	5.854	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.625	0.000	0.030	0.000	1	100	NaN	NaN	0.624	0.000	4.204	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.151	0.029	0.000	0.004	1	100	0.975	0.978	0.487	0.003	8.531	0.076	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.001	0.000	5.219	2.321	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.688	0.000	0.030	0.000	-1	100	NaN	NaN	0.654	0.000	4.111	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.288	0.007	0.000	0.002	-1	100	0.975	0.959	0.090	0.001	25.555	0.248	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	23.019	11.592	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.620	0.000	0.031	0.000	1	5	NaN	NaN	0.626	0.000	4.186	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.279	0.011	0.000	0.001	1	5	0.975	0.978	0.489	0.009	2.615	0.054	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.001	0.000	1.712	0.740	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.739	0.000	0.022	0.000	-1	5	NaN	NaN	0.416	0.000	1.775	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.001	0.001	0.000	-1	5	0.979	0.984	0.001	0.000	23.965	6.157	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	17.618	13.194	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.736	0.000	0.022	0.000	1	1	NaN	NaN	0.416	0.000	1.770	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.022	0.001	0.001	0.000	1	1	0.971	0.984	0.001	0.000	20.604	5.305	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.660	4.456	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.728	0.000	0.022	0.000	-1	1	NaN	NaN	0.415	0.000	1.754	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.002	0.001	0.000	-1	1	0.971	0.975	0.001	0.000	33.707	10.994	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.668	14.233	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.735	0.000	0.022	0.000	1	100	NaN	NaN	0.443	0.000	1.657	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.003	0.000	0.000	1	100	0.983	0.985	0.006	0.001	7.356	0.989	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.527	4.055	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.723	0.000	0.022	0.000	-1	100	NaN	NaN	0.417	0.000	1.734	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.038	0.002	0.000	0.000	-1	100	0.983	0.975	0.001	0.000	54.783	15.504	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.947	15.827	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.725	0.000	0.022	0.000	1	5	NaN	NaN	0.423	0.000	1.713	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	5	0.979	0.985	0.007	0.001	3.490	0.563	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.026	3.388	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.600	0.0	0.800	0.000	k-means++	NaN	30	NaN	0.363	0.0	1.653	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.382	0.000	k-means++	0.001	30	0.001	0.000	0.0	9.402	6.605	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	11.181	7.798	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.474	0.0	1.012	0.000	random	NaN	30	NaN	0.413	0.0	1.147	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.383	0.000	random	0.001	30	0.000	0.000	0.0	9.478	6.187	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.036	6.698	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	5.801	0.0	4.137	0.000	k-means++	NaN	30	NaN	2.556	0.0	2.270	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.948	0.000	k-means++	0.002	30	0.001	0.000	0.0	5.890	2.602	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.863	5.735	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.314	0.0	4.516	0.000	random	NaN	30	NaN	2.706	0.0	1.964	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.001	0.0	16.036	0.000	random	0.002	30	0.002	0.000	0.0	5.838	2.620	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	random	1.000	30	1.000	0.000	0.0	9.794	5.509	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.073	0.0	0.044	0.000	random	NaN	20	NaN	0.084	0.0	0.865	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.195	0.000	random	0.001	20	-0.002	0.001	0.0	2.768	0.453	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.812	5.560	See	See
3	KMeans_short	fit	10000	10000	2	20	0.214	0.0	0.015	0.000	k-means++	NaN	20	NaN	0.029	0.0	7.354	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.194	0.000	k-means++	-0.001	20	-0.001	0.001	0.0	2.688	0.558	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.600	5.506	See	See
6	KMeans_short	fit	10000	10000	100	20	0.186	0.0	0.860	0.000	random	NaN	20	NaN	0.322	0.0	0.578	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.914	0.000	random	0.300	20	0.319	0.001	0.0	2.154	0.332	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	8.287	4.194	See	See
9	KMeans_short	fit	10000	10000	100	20	0.552	0.0	0.290	0.000	k-means++	NaN	20	NaN	0.124	0.0	4.443	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.807	0.000	k-means++	0.266	20	0.324	0.001	0.0	2.249	0.350	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.103	4.036	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	9.969	0.0	[-0.11835882]	0.000	NaN	NaN	NaN	NaN	NaN	1.710	0.0	5.831	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[59.62033771]	0.000	NaN	NaN	NaN	NaN	0.567	0.000	0.0	0.818	0.377	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.24690939]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.398	0.386	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.725	0.0	[2.86939723]	0.001	NaN	NaN	NaN	NaN	NaN	0.634	0.0	1.144	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[137.0560299]	0.000	NaN	NaN	NaN	NaN	0.250	0.003	0.0	0.548	0.096	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[25.25451821]	0.000	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.123	0.090	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.168	0.0	0.477	0.0	NaN	NaN	NaN	0.171	0.0	0.983	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	8.322	0.0	NaN	NaN	0.1	0.016	0.0	0.595	0.028	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.246	0.0	NaN	NaN	NaN	0.000	0.0	0.648	0.606	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.279	0.0	0.625	0.0	NaN	NaN	NaN	0.221	0.0	5.777	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.122	0.0	NaN	NaN	1.0	0.000	0.0	0.713	0.491	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.013	0.0	NaN	NaN	NaN	0.000	0.0	0.688	0.657	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()

Raw results

Randomized HPO Curves

25th percentile

Mean

75th percentile